Prediction of Maize Price using Hidden Markov Chain Model: An Application on Grand market of Lome (TOGO)

International Journal of Mathematics Trends and Technology (IJMTT)
© 2022 by IJMTT Journal
Volume-68 Issue-6
Year of Publication : 2022
Authors : Marc Marin Agbodjan, Joseph Mung’atu, Anthony Wanjoya

How to Cite?

Marc Marin Agbodjan, Joseph Mung’atu, Anthony Wanjoya, " Prediction of Maize Price using Hidden Markov Chain Model: An Application on Grand market of Lome (TOGO) ," International Journal of Mathematics Trends and Technology, vol. 68, no. 6, pp. 117-130, 2022. Crossref,

Around the world, the Hidden Markov Models (HMM) are the most popular methods in the machine learning and statistics for modeling sequences, especially in speech recognition domain. According to the number of patent applications for speech recognition technology form 1988 to 1998, the trend shows that this method has become very mature. The Hidden Markov Model (HMM) is a stochastic model where the modeled system is considered as a Markov process with unknown parameters. The challenge is to determine the hidden parameters of the observed parameters. It provides a probabilistic framework for time series forecast modeling. If the causal factors are not directly observed and have the properties of the Markov chain, therefore, a pair of observations and its causative factors are hidden Markov model. The proposed model was applied to a variation in the price of cereal products on the Togolese market between January 2015 and December 2021. The price of these cereal products was influenced by several factors, such as the international recession, government policy, climate, etc. The Forward-Backward, Viterbi and Baum-Welch algorithm was used to estimate the estdimated parameters were used to calculate the expected state of price for those grain product to find the most likely sequence and our proposed model would be coded using Matlab.

Keywords : Hidden Markov Chain(HMM), Transition Probability Matrix, Emission Probability, Maize, Estimated parameters.


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